{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b4e05e11-17a0-4fb2-85e3-c2f4e060d61f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mtrain_SI: \u001b[0mweights=./runs/train/fog_02/weights/best.pt, cfg=models/yolov5s_kitti.yaml, data=data/kitti.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=60, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, evolve_population=data/hyps, resume_evolve=None, bucket=, cache=None, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs/train, name=exp, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=1, artifact_alias=latest, ndjson_console=False, ndjson_file=False, ewc_pt=None, ewc_lambda=0.0, SI_enable=True, SI_pt=./runs/train/fog_02/weights/si.pt, SI_lambda=0.005\n",
      "Command 'git fetch ultralytics' timed out after 5 seconds\n",
      "YOLOv5 🚀 68de71e8 Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA vGPU-32GB, 32260MiB)\n",
      "\n",
      "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0\n",
      "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\n",
      "fatal: unable to access 'https://github.com/ultralytics/yolov5/': GnuTLS recv error (-110): The TLS connection was non-properly terminated.\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Experiment is live on comet.com \u001b[38;5;39mhttps://www.comet.com/nagasaki-soyorin/yolov5/f6e4787b086d458fbf4635e3765e36cc\u001b[0m\n",
      "\n",
      "\n",
      "                 from  n    params  module                                  arguments                     \n",
      "  0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]              \n",
      "  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                \n",
      "  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   \n",
      "  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
      "  4                -1  2    115712  models.common.C3                        [128, 128, 2]                 \n",
      "  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              \n",
      "  6                -1  3    625152  models.common.C3                        [256, 256, 3]                 \n",
      "  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              \n",
      "  8                -1  1   1182720  models.common.C3                        [512, 512, 1]                 \n",
      "  9                -1  1    656896  models.common.SPPF                      [512, 512, 5]                 \n",
      " 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
      " 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 12           [-1, 6]  1         0  models.common.Concat                    [1]                           \n",
      " 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          \n",
      " 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
      " 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 16           [-1, 4]  1         0  models.common.Concat                    [1]                           \n",
      " 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          \n",
      " 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              \n",
      " 19          [-1, 14]  1         0  models.common.Concat                    [1]                           \n",
      " 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          \n",
      " 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n",
      " 22          [-1, 10]  1         0  models.common.Concat                    [1]                           \n",
      " 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          \n",
      " 24      [17, 20, 23]  1     35061  models.yolo.Detect                      [8, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
      "YOLOv5s_kitti summary: 214 layers, 7041205 parameters, 7041205 gradients, 16.0 GFLOPs\n",
      "\n",
      "Transferred 348/349 items from runs/train/fog_02/weights/best.pt\n",
      "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
      "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 60 weight(decay=0.0005), 60 bias\n",
      "\u001b[34m\u001b[1malbumentations: \u001b[0m1 validation error for InitSchema\n",
      "size\n",
      "  Field required [type=missing, input_value={'height': 640, 'width': ...'mask_interpolation': 0}, input_type=dict]\n",
      "    For further information visit https://errors.pydantic.dev/2.10/v/missing\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /root/autodl-tmp/datasets/kitti/labels/train.cache... 4189 image\u001b[0m\n",
      "\u001b[34m\u001b[1mval: \u001b[0mScanning /root/autodl-tmp/datasets/kitti/labels/val.cache... 1048 images, 0\u001b[0m\n",
      "\n",
      "\u001b[34m\u001b[1mAutoAnchor: \u001b[0m4.81 anchors/target, 0.999 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅\n",
      "Plotting labels to runs/train/exp31/labels.jpg... \n",
      "Image sizes 640 train, 640 val\n",
      "Using 8 dataloader workers\n",
      "Logging results to \u001b[1mruns/train/exp31\u001b[0m\n",
      "Starting training for 60 epochs...\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       0/59      3.61G    0.03491    0.03427   0.006935        128        640: 1\n",
      "tensor([0.88930], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00010], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.657      0.403      0.453      0.259\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       1/59      3.61G    0.03378    0.03058   0.005272        133        640: 1\n",
      "tensor([0.90297], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00016], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.741      0.491      0.572      0.336\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       2/59      3.61G    0.03642    0.03339   0.006735        131        640: 1\n",
      "tensor([1.04583], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00022], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.604      0.358      0.394      0.217\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       3/59      3.61G     0.0379    0.03416   0.006862        108        640: 1\n",
      "tensor([0.85105], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00045], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.63      0.421      0.474      0.253\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       4/59      3.61G    0.03806    0.03317   0.006185        156        640: 1\n",
      "tensor([0.98744], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00071], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.692      0.519      0.573       0.31\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       5/59      3.61G    0.03723    0.03267   0.005914        123        640: 1\n",
      "tensor([0.90283], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00090], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.712      0.462      0.525      0.302\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       6/59      3.61G    0.03631    0.03144   0.005207        174        640: 1\n",
      "tensor([1.00477], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00104], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.786      0.541       0.63      0.355\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       7/59      3.61G    0.03572    0.03059   0.004944        166        640: 1\n",
      "tensor([1.04483], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00111], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.803       0.58      0.682      0.392\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       8/59      3.61G    0.03494    0.03062   0.004671        152        640: 1\n",
      "tensor([0.92701], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00118], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.767      0.585      0.646      0.362\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       9/59      3.61G    0.03455    0.03019   0.004494        136        640: 1\n",
      "tensor([0.85182], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00123], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.77      0.627      0.703      0.412\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      10/59      3.61G    0.03403    0.02984   0.004322        134        640: 1\n",
      "tensor([0.85015], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00128], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.848      0.616       0.71      0.412\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      11/59      3.61G    0.03336    0.02912   0.004199        182        640: 1\n",
      "tensor([0.94433], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00132], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.766       0.59      0.656      0.383\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      12/59      3.61G    0.03354    0.02942   0.004002        128        640: 1\n",
      "tensor([0.74584], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00136], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.839      0.643      0.731      0.414\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      13/59      3.61G    0.03275    0.02867   0.003978        112        640: 1\n",
      "tensor([0.89814], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00140], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.821      0.615      0.701      0.396\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      14/59      3.61G    0.03276    0.02888   0.004049        151        640: 1\n",
      "tensor([0.83350], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00143], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.846      0.618      0.719      0.417\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      15/59      3.61G     0.0324    0.02874   0.003732        132        640: 1\n",
      "tensor([0.82720], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00146], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.822      0.636      0.722      0.417\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      16/59      3.61G    0.03194    0.02808   0.003721        131        640: 1\n",
      "tensor([0.80395], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00147], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.863      0.607       0.72      0.423\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      17/59      3.61G    0.03221    0.02813   0.003671        159        640: 1\n",
      "tensor([0.89314], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00149], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.847      0.657      0.739      0.431\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      18/59      3.61G    0.03149    0.02772   0.003551        125        640: 1\n",
      "tensor([0.70578], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00151], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.862      0.612      0.713      0.419\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      19/59      3.61G    0.03146    0.02797   0.003565         88        640: 1\n",
      "tensor([0.66952], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00152], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.78      0.669       0.72      0.435\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      20/59      3.61G    0.03087    0.02699   0.003475        137        640: 1\n",
      "tensor([0.88889], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00153], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.831      0.622      0.713      0.422\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      21/59      3.61G    0.03078    0.02755   0.003399        166        640: 1\n",
      "tensor([0.92228], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00154], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.811      0.683      0.757      0.456\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      22/59      3.61G    0.03056    0.02715   0.003296        161        640: 1\n",
      "tensor([0.87083], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00155], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.811      0.651      0.726      0.425\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      23/59      3.61G    0.03039    0.02672   0.003309        118        640: 1\n",
      "tensor([0.75525], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00156], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.847      0.656      0.738      0.456\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      24/59      3.61G    0.03005    0.02661   0.003186        151        640: 1\n",
      "tensor([0.82461], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00156], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.897      0.664      0.762       0.45\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      25/59      3.61G    0.02984    0.02672   0.003158        133        640: 1\n",
      "tensor([0.76368], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00156], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.83      0.656      0.724      0.429\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      26/59      3.61G    0.02962    0.02646   0.003141        154        640: 1\n",
      "tensor([0.89677], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00157], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.873       0.65      0.758      0.456\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      27/59      3.61G    0.02954    0.02652   0.003213        122        640: 1\n",
      "tensor([0.74169], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00157], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.875      0.632      0.741      0.444\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      28/59      3.61G    0.02941    0.02631   0.003026        123        640: 1\n",
      "tensor([0.64678], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00157], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.896      0.659      0.761      0.454\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      29/59      3.61G    0.02914    0.02595   0.002967        127        640: 1\n",
      "tensor([0.65209], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00157], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.848      0.617      0.722      0.427\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      30/59      3.61G    0.02885    0.02503   0.002894        127        640: 1\n",
      "tensor([0.68373], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00158], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.862      0.644      0.739      0.447\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      31/59      3.61G    0.02885    0.02556   0.002922        122        640: 1\n",
      "tensor([0.75989], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00157], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.841      0.642      0.733      0.432\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      32/59      3.61G    0.02871    0.02574   0.002836        146        640: 1\n",
      "tensor([0.80165], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00157], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.865      0.669      0.755      0.446\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      33/59      3.61G    0.02845     0.0251   0.002807        202        640: 1\n",
      "tensor([0.87609], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00157], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.911      0.639       0.75      0.449\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      34/59      3.61G    0.02816    0.02495   0.002792         94        640: 1\n",
      "tensor([0.58369], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00157], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.891      0.649      0.756      0.464\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      35/59      3.61G    0.02806     0.0249   0.002735        152        640: 1\n",
      "tensor([0.77116], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00156], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.884      0.668      0.765      0.466\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      36/59      3.61G    0.02774    0.02464   0.002742        123        640: 1\n",
      "tensor([0.65854], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00156], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.814      0.693      0.753      0.463\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      37/59      3.61G    0.02758    0.02474   0.002756        162        640: 1\n",
      "tensor([0.70223], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00155], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.845      0.657      0.737      0.453\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      38/59      3.61G    0.02767    0.02496   0.002737        161        640: 1\n",
      "tensor([0.70832], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00155], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.899      0.633      0.745       0.46\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      39/59      3.61G    0.02735    0.02465   0.002649        122        640: 1\n",
      "tensor([0.63323], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00155], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.882      0.659       0.76      0.461\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      40/59      3.61G     0.0273    0.02432   0.002655        126        640: 1\n",
      "tensor([0.63675], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00155], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.889      0.615      0.723      0.441\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      41/59      3.61G    0.02691    0.02425   0.002609         90        640: 1\n",
      "tensor([0.60695], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00154], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.891      0.655      0.755      0.464\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      42/59      3.61G    0.02678    0.02401   0.002442        118        640: 1\n",
      "tensor([0.68348], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00154], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.91      0.631      0.742      0.465\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      43/59      3.61G    0.02666    0.02399   0.002337        157        640: 1\n",
      "tensor([0.73954], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00154], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.889      0.666      0.755      0.474\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      44/59      3.61G    0.02649    0.02385   0.002458        104        640: 1\n",
      "tensor([0.52512], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00153], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.899      0.654      0.748      0.467\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      45/59      3.61G    0.02654    0.02413   0.002321        157        640: 1\n",
      "tensor([0.68081], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00152], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.871      0.668      0.762      0.473\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      46/59      3.61G    0.02632    0.02344   0.002272        108        640: 1\n",
      "tensor([0.52835], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00152], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.84        0.7      0.775      0.477\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      47/59      3.61G    0.02621    0.02341   0.002343        159        640: 1\n",
      "tensor([0.68200], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00152], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.839      0.689      0.765       0.47\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      48/59      3.61G    0.02593    0.02329   0.002304        118        640: 1\n",
      "tensor([0.62291], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00151], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.878       0.67      0.761      0.477\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      49/59      3.61G    0.02596    0.02357   0.002286        176        640: 1\n",
      "tensor([0.83369], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00151], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.881      0.649       0.76      0.477\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      50/59      3.61G      0.026     0.0232   0.002312        130        640: 1\n",
      "tensor([0.62543], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00150], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.882      0.661      0.766      0.482\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      51/59      3.61G     0.0255    0.02323   0.002164        178        640: 1\n",
      "tensor([0.81232], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00150], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.851      0.682      0.774      0.485\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      52/59      3.61G    0.02546    0.02287   0.002208        148        640: 1\n",
      "tensor([0.64691], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00149], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.883      0.666      0.766      0.482\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      53/59      3.61G    0.02537    0.02276   0.002152        115        640: 1\n",
      "tensor([0.61348], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00149], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.86      0.687      0.765       0.48\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      54/59      3.61G    0.02531    0.02259    0.00219        124        640: 1\n",
      "tensor([0.58217], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00149], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.856      0.667      0.748      0.475\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      55/59      3.61G    0.02498    0.02222   0.002187        163        640: 1\n",
      "tensor([0.63480], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00148], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.871      0.665      0.756      0.475\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      56/59      3.61G    0.02495    0.02254   0.002132        200        640: 1\n",
      "tensor([0.73353], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00148], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.921      0.633      0.756      0.476\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      57/59      3.61G    0.02494     0.0225   0.002147        141        640: 1\n",
      "tensor([0.63796], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00148], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.887      0.672      0.765       0.48\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      58/59      3.61G    0.02506    0.02242   0.002174        146        640: 1\n",
      "tensor([0.63181], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00147], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.868      0.688      0.767      0.479\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      59/59      3.61G    0.02483    0.02228   0.002141        168        640: 1\n",
      "tensor([0.64789], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00147], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.897      0.668      0.764      0.487\n",
      "\n",
      "60 epochs completed in 1.079 hours.\n",
      "Optimizer stripped from runs/train/exp31/weights/last.pt, 14.3MB\n",
      "Optimizer stripped from runs/train/exp31/weights/best.pt, 14.3MB\n",
      "\n",
      "Validating runs/train/exp31/weights/best.pt...\n",
      "Fusing layers... \n",
      "YOLOv5s_kitti summary: 157 layers, 7031701 parameters, 0 gradients, 15.8 GFLOPs\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.898      0.668      0.764      0.488\n",
      "                   Car       1048       4012      0.928        0.8      0.908      0.661\n",
      "                   Van       1048        431      0.918      0.724      0.833      0.601\n",
      "                 Truck       1048        166      0.903      0.786      0.875      0.633\n",
      "                  Tram       1048         56      0.912      0.742      0.798      0.481\n",
      "            Pedestrian       1048        618      0.835      0.584      0.693      0.349\n",
      "        Person_sitting       1048         20      0.943       0.55      0.641      0.385\n",
      "               Cyclist       1048        234      0.908      0.551      0.667      0.375\n",
      "                  Misc       1048        138      0.839      0.603      0.699       0.42\n",
      "Results saved to \u001b[1mruns/train/exp31\u001b[0m\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Comet.ml Experiment Summary\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Data:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     display_summary_level : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     name                  : exp\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     url                   : \u001b[38;5;39mhttps://www.comet.com/nagasaki-soyorin/yolov5/f6e4787b086d458fbf4635e3765e36cc\u001b[0m\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Metrics [count] (min, max):\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_f1                         : 0.8593563439076883\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_false_positives            : 247.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_mAP@.5                     : 0.9077793884084308\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_mAP@.5:.95                 : 0.6607936466287194\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_precision                  : 0.9284279246348792\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_recall                     : 0.7998504486540379\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_support                    : 4012\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_true_positives             : 3209.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_f1                     : 0.6860753861657228\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_false_positives        : 13.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_mAP@.5                 : 0.6667532018619352\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_mAP@.5:.95             : 0.3747071014266789\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_precision              : 0.908118350696899\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_recall                 : 0.5512820512820513\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_support                : 234\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_true_positives         : 129.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_f1                        : 0.7012290252253902\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_false_positives           : 16.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_mAP@.5                    : 0.6994332170630888\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_mAP@.5:.95                : 0.42011349295032935\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_precision                 : 0.8386220730208553\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_recall                    : 0.6025174469685092\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_support                   : 138\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_true_positives            : 83.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_f1                  : 0.6872580209419786\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_false_positives     : 72.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_mAP@.5              : 0.6925685259011038\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_mAP@.5:.95          : 0.34912793920822105\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_precision           : 0.8345825478630627\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_recall              : 0.5841423948220065\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_support             : 618\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_true_positives      : 361.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_f1              : 0.6946846042288222\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_false_positives : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_mAP@.5          : 0.6405976971720088\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_mAP@.5:.95      : 0.3848762414457153\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_precision       : 0.942664740378021\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_recall          : 0.55\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_support         : 20\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_true_positives  : 11.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_f1                        : 0.8183902128288193\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_false_positives           : 4.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_mAP@.5                    : 0.7978961220771291\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_mAP@.5:.95                : 0.4808795556892277\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_precision                 : 0.9121869206814343\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_recall                    : 0.7420844902257542\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_support                   : 56\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_true_positives            : 42.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_f1                       : 0.840705633797954\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_false_positives          : 14.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_mAP@.5                   : 0.8752188509490784\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_mAP@.5:.95               : 0.6325972060540781\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_precision                : 0.9031357304106347\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_recall                   : 0.7863485589389204\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_support                  : 166\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_true_positives           : 131.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_f1                         : 0.8096321186481309\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_false_positives            : 28.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_mAP@.5                     : 0.8329917775140466\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_mAP@.5:.95                 : 0.6014030959332888\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_precision                  : 0.9176929905582657\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_recall                     : 0.7243391883763114\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_support                    : 431\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_true_positives             : 312.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     loss [1566]                    : (0.590071439743042, 2.1990065574645996)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/mAP_0.5 [120]          : (0.39438884842630206, 0.774743654242879)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/mAP_0.5:0.95 [120]     : (0.21722069329832805, 0.48735465689881585)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/precision [120]        : (0.6043350031521292, 0.9207030848663871)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/recall [120]           : (0.35830539575845777, 0.6999375764541298)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/box_loss [120]           : (0.02483157441020012, 0.038060273975133896)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/cls_loss [120]           : (0.0021317689679563046, 0.006935194134712219)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/obj_loss [120]           : (0.022224027663469315, 0.03426550328731537)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/box_loss [120]             : (0.03204795718193054, 0.04583429917693138)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/cls_loss [120]             : (0.004761336836963892, 0.020212717354297638)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/obj_loss [120]             : (0.05031146481633186, 0.0748109519481659)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr0 [120]                    : (0.00043, 0.07011450381679389)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr1 [120]                    : (0.00043, 0.009657697201017812)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr2 [120]                    : (0.00043, 0.009657697201017812)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Others:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Name                        : exp\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Run Path                    : nagasaki-soyorin/yolov5/f6e4787b086d458fbf4635e3765e36cc\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_batch_metrics     : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_confusion_matrix  : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_per_class_metrics : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_max_image_uploads     : 100\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_mode                  : online\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_model_name            : yolov5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hasNestedParams             : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Parameters:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     anchor_t            : 4.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     artifact_alias      : latest\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     batch_size          : 16\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     box                 : 0.05\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bucket              : \n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cache               : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cls                 : 0.05000000000000001\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cls_pw              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     copy_paste          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cos_lr              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     degrees             : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     device              : \n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     entity              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     evolve              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     evolve_population   : data/hyps\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ewc_lambda          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ewc_pt              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     exist_ok            : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     fl_gamma            : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     fliplr              : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     flipud              : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     freeze              : [0]\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_h               : 0.015\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_s               : 0.7\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_v               : 0.4\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|anchor_t        : 4.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|box             : 0.05\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|cls             : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|cls_pw          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|copy_paste      : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|degrees         : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|fl_gamma        : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|fliplr          : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|flipud          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_h           : 0.015\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_s           : 0.7\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_v           : 0.4\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|iou_t           : 0.2\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|lr0             : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|lrf             : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|mixup           : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|momentum        : 0.937\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|mosaic          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|obj             : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|obj_pw          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|perspective     : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|scale           : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|shear           : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|translate       : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_bias_lr  : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_epochs   : 3.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_momentum : 0.8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|weight_decay    : 0.0005\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     image_weights       : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     imgsz               : 640\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     iou_t               : 0.2\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     label_smoothing     : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     local_rank          : -1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     lr0                 : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     lrf                 : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     mixup               : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     momentum            : 0.937\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     mosaic              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     multi_scale         : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     name                : exp\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ndjson_console      : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ndjson_file         : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noautoanchor        : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noplots             : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     nosave              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noval               : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     obj                 : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     obj_pw              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     optimizer           : SGD\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     patience            : 100\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     perspective         : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     project             : runs/train\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     quad                : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     rect                : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     resume              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     resume_evolve       : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_dir            : runs/train/exp31\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_period         : -1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     scale               : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     seed                : 0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     shear               : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     single_cls          : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sync_bn             : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     translate           : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     upload_dataset      : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val_conf_threshold  : 0.001\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val_iou_threshold   : 0.6\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_bias_lr      : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_epochs       : 3.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_momentum     : 0.8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     weight_decay        : 0.0005\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     workers             : 8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Uploads:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     asset                        : 13 (1.90 MB)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conda-environment-definition : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conda-info                   : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conda-specification          : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     confusion-matrix             : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     environment details          : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     git metadata                 : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     images                       : 106\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     installed packages           : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     model graph                  : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     os packages                  : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m \n"
     ]
    }
   ],
   "source": [
    "command = f\"\"\"\n",
    "env COMET_LOG_PER_CLASS_METRICS=true python train_SI.py \\\n",
    "--img 640 \\\n",
    "--bbox_interval 1 \\\n",
    "--cfg models/yolov5s_kitti.yaml \\\n",
    "--data data/kitti.yaml \\\n",
    "--epochs 60 \\\n",
    "--weights ./runs/train/fog_02/weights/best.pt \\\n",
    "--SI_enable \\\n",
    "--SI_pt ./runs/train/fog_02/weights/si.pt \\\n",
    "--SI_lambda 5e-3 \\\n",
    "\"\"\"\n",
    "!{command}\n",
    "# --weights ./runs/train/exp3/weights/best.pt \\\n",
    "# 这个吃完饭回来后再看。\n",
    "# 这个目前是1.0强度增量的最好参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f0db6f8c-b233-49e5-93a1-93b440d609c8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mtrain_SI: \u001b[0mweights=./runs/train/fog_02/weights/best.pt, cfg=models/yolov5s_kitti.yaml, data=data/kitti.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=100, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, evolve_population=data/hyps, resume_evolve=None, bucket=, cache=None, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs/train, name=exp, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=1, artifact_alias=latest, ndjson_console=False, ndjson_file=False, ewc_pt=None, ewc_lambda=0.0, SI_enable=True, SI_pt=./runs/train/fog_02/weights/si.pt, SI_lambda=0.005\n",
      "Command 'git fetch ultralytics' timed out after 5 seconds\n",
      "YOLOv5 🚀 68de71e8 Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA vGPU-32GB, 32260MiB)\n",
      "\n",
      "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0\n",
      "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\n",
      "fatal: unable to access 'https://github.com/ultralytics/yolov5/': GnuTLS recv error (-110): The TLS connection was non-properly terminated.\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Experiment is live on comet.com \u001b[38;5;39mhttps://www.comet.com/nagasaki-soyorin/yolov5/1e393e7602234cc89cb25c6969efd22e\u001b[0m\n",
      "\n",
      "\n",
      "                 from  n    params  module                                  arguments                     \n",
      "  0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]              \n",
      "  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                \n",
      "  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   \n",
      "  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
      "  4                -1  2    115712  models.common.C3                        [128, 128, 2]                 \n",
      "  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              \n",
      "  6                -1  3    625152  models.common.C3                        [256, 256, 3]                 \n",
      "  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              \n",
      "  8                -1  1   1182720  models.common.C3                        [512, 512, 1]                 \n",
      "  9                -1  1    656896  models.common.SPPF                      [512, 512, 5]                 \n",
      " 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
      " 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 12           [-1, 6]  1         0  models.common.Concat                    [1]                           \n",
      " 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          \n",
      " 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
      " 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 16           [-1, 4]  1         0  models.common.Concat                    [1]                           \n",
      " 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          \n",
      " 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              \n",
      " 19          [-1, 14]  1         0  models.common.Concat                    [1]                           \n",
      " 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          \n",
      " 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n",
      " 22          [-1, 10]  1         0  models.common.Concat                    [1]                           \n",
      " 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          \n",
      " 24      [17, 20, 23]  1     35061  models.yolo.Detect                      [8, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
      "YOLOv5s_kitti summary: 214 layers, 7041205 parameters, 7041205 gradients, 16.0 GFLOPs\n",
      "\n",
      "Transferred 348/349 items from runs/train/fog_02/weights/best.pt\n",
      "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
      "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 60 weight(decay=0.0005), 60 bias\n",
      "\u001b[34m\u001b[1malbumentations: \u001b[0m1 validation error for InitSchema\n",
      "size\n",
      "  Field required [type=missing, input_value={'height': 640, 'width': ...'mask_interpolation': 0}, input_type=dict]\n",
      "    For further information visit https://errors.pydantic.dev/2.10/v/missing\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /root/autodl-tmp/datasets/kitti/labels/train.cache... 4189 image\u001b[0m\n",
      "\u001b[34m\u001b[1mval: \u001b[0mScanning /root/autodl-tmp/datasets/kitti/labels/val.cache... 1048 images, 0\u001b[0m\n",
      "\n",
      "\u001b[34m\u001b[1mAutoAnchor: \u001b[0m4.81 anchors/target, 0.999 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅\n",
      "Plotting labels to runs/train/exp65/labels.jpg... \n",
      "Image sizes 640 train, 640 val\n",
      "Using 8 dataloader workers\n",
      "Logging results to \u001b[1mruns/train/exp65\u001b[0m\n",
      "Starting training for 100 epochs...\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       0/99      3.61G    0.03491    0.03427   0.006935        128        640: 1\n",
      "tensor([0.88930], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00010], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.662       0.39      0.433      0.261\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       1/99      3.61G    0.03379     0.0307   0.005475        133        640: 1\n",
      "tensor([0.96269], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00015], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.514      0.222      0.237      0.131\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       2/99      3.61G     0.0363    0.03286    0.00623        131        640: 1\n",
      "tensor([0.97788], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00021], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.454      0.215      0.219      0.116\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       3/99      3.61G    0.03828    0.03438   0.007073        108        640: 1\n",
      "tensor([0.84067], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00044], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.505      0.262      0.253      0.134\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       4/99      3.61G    0.03831    0.03367   0.006651        156        640: 1\n",
      "tensor([1.05089], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00075], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675        0.6      0.418       0.43      0.222\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       5/99      3.61G    0.03748    0.03241   0.005797        123        640: 1\n",
      "tensor([0.87428], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00092], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.807      0.644      0.725      0.412\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       6/99      3.61G    0.03637    0.03137   0.005124        174        640: 1\n",
      "tensor([1.01861], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00103], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.795      0.646      0.727      0.428\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       7/99      3.61G    0.03571    0.03051   0.004996        166        640: 1\n",
      "tensor([1.05442], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00111], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.817      0.697      0.769      0.465\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       8/99      3.61G    0.03523    0.03064   0.004672        152        640: 1\n",
      "tensor([0.92666], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00118], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.861       0.66      0.769      0.467\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       9/99      3.61G    0.03477    0.03052   0.004779        136        640: 1\n",
      "tensor([0.90417], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00125], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.829      0.633       0.73      0.428\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      10/99      3.61G    0.03479    0.03022    0.00456        134        640: 1\n",
      "tensor([0.86765], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00133], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675        0.8      0.682      0.757      0.468\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      11/99      3.61G    0.03388    0.02937    0.00435        182        640: 1\n",
      "tensor([0.95272], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00138], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.832      0.692      0.786      0.485\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      12/99      3.61G    0.03407    0.02987   0.004326        128        640: 1\n",
      "tensor([0.76691], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00143], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.86      0.731      0.822      0.508\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      13/99      3.61G    0.03344    0.02901   0.004043        112        640: 1\n",
      "tensor([0.91968], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00148], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.86      0.728      0.808      0.493\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      14/99      3.61G    0.03325    0.02909   0.004034        151        640: 1\n",
      "tensor([0.86243], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00151], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.887      0.734      0.829      0.519\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      15/99      3.61G    0.03291    0.02893   0.003833        132        640: 1\n",
      "tensor([0.83985], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00154], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.848      0.769      0.845      0.527\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      16/99      3.61G    0.03246    0.02852    0.00386        131        640: 1\n",
      "tensor([0.82723], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00156], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.874      0.728      0.817        0.5\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      17/99      3.61G    0.03292    0.02856   0.003848        159        640: 1\n",
      "tensor([0.98781], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00159], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.882      0.721      0.844      0.529\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      18/99      3.61G    0.03245    0.02833   0.003835        125        640: 1\n",
      "tensor([0.73740], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00162], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.813      0.707      0.784      0.483\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      19/99      3.61G    0.03231    0.02855   0.003735         88        640: 1\n",
      "tensor([0.74671], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00167], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.884      0.765      0.849      0.536\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      20/99      3.61G    0.03193    0.02768    0.00365        137        640: 1\n",
      "tensor([0.90563], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00169], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.899      0.753      0.851      0.542\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      21/99      3.61G    0.03176    0.02828   0.003647        166        640: 1\n",
      "tensor([0.91971], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00170], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.879      0.724      0.818      0.536\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      22/99      3.61G    0.03162    0.02784   0.003514        161        640: 1\n",
      "tensor([0.88762], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00173], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.894      0.742      0.838      0.549\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      23/99      3.61G     0.0315    0.02736   0.003527        118        640: 1\n",
      "tensor([0.75259], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00174], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.908      0.752      0.847      0.547\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      24/99      3.61G    0.03125     0.0274   0.003451        151        640: 1\n",
      "tensor([0.87590], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00175], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.914      0.746      0.848      0.543\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      25/99      3.61G    0.03093    0.02755   0.003394        133        640: 1\n",
      "tensor([0.76226], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00176], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.93       0.76      0.852      0.556\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      26/99      3.61G    0.03103    0.02723   0.003325        154        640: 1\n",
      "tensor([0.89836], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00178], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.906      0.764      0.848      0.555\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      27/99      3.61G    0.03091    0.02741   0.003451        122        640: 1\n",
      "tensor([0.75669], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00179], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.875      0.773      0.839      0.542\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      28/99      3.61G    0.03068    0.02719    0.00328        123        640: 1\n",
      "tensor([0.64887], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00180], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.911      0.782      0.853      0.567\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      29/99      3.61G    0.03044    0.02673   0.003205        127        640: 1\n",
      "tensor([0.69245], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00180], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.886      0.752      0.833      0.546\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      30/99      3.61G    0.03023    0.02593   0.003136        127        640: 1\n",
      "tensor([0.68877], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00181], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.875      0.744      0.842      0.544\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      31/99      3.61G    0.03029    0.02651   0.003201        122        640: 1\n",
      "tensor([0.77791], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00182], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.916      0.771      0.859      0.576\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      32/99      3.61G    0.03014    0.02673   0.003122        146        640: 1\n",
      "tensor([0.84955], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00183], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.892      0.794      0.858      0.573\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      33/99      3.61G    0.02971    0.02611   0.003126        202        640: 1\n",
      "tensor([0.93866], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00183], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.878      0.762      0.841      0.559\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      34/99      3.61G    0.02981    0.02605   0.003211         94        640: 1\n",
      "tensor([0.65740], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00184], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.889      0.798      0.859      0.573\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      35/99      3.61G     0.0296    0.02606    0.00309        152        640: 1\n",
      "tensor([0.80939], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00184], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.898      0.807      0.868      0.587\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      36/99      3.61G    0.02951    0.02587   0.003064        123        640: 1\n",
      "tensor([0.68827], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00184], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.908      0.773      0.863      0.582\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      37/99      3.61G    0.02946    0.02603   0.003096        162        640: 1\n",
      "tensor([0.75842], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00185], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675        0.9      0.789      0.856      0.572\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      38/99      3.61G    0.02944    0.02621   0.003077        161        640: 1\n",
      "tensor([0.78806], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00185], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.872       0.81      0.877      0.589\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      39/99      3.61G    0.02929    0.02596   0.002997        122        640: 1\n",
      "tensor([0.68886], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00186], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.908       0.78       0.87      0.583\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      40/99      3.61G    0.02901    0.02558   0.003015        126        640: 1\n",
      "tensor([0.66520], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00187], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.892      0.796      0.872      0.583\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      41/99      3.61G    0.02894    0.02575   0.003044         90        640: 1\n",
      "tensor([0.62404], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00187], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.855      0.545      0.645      0.411\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      42/99      3.61G    0.02878    0.02554   0.002823        118        640: 1\n",
      "tensor([0.74903], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00189], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.927      0.777      0.872      0.588\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      43/99      3.61G    0.02888     0.0256   0.002744        157        640: 1\n",
      "tensor([0.77269], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00190], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.902      0.816      0.877      0.593\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      44/99      3.61G    0.02875    0.02546   0.002843        104        640: 1\n",
      "tensor([0.57384], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00190], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.909      0.804      0.878      0.594\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      45/99      3.61G    0.02852    0.02564   0.002696        157        640: 1\n",
      "tensor([0.75924], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00190], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.91      0.819      0.884      0.601\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      46/99      3.61G    0.02841      0.025   0.002686        108        640: 1\n",
      "tensor([0.55889], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00190], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.903        0.8      0.868      0.591\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      47/99      3.61G    0.02843    0.02507   0.002742        159        640: 1\n",
      "tensor([0.76517], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00190], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.893      0.832       0.88        0.6\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      48/99      3.61G    0.02807    0.02494   0.002772        118        640: 1\n",
      "tensor([0.68532], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00190], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.899      0.813      0.878      0.601\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      49/99      3.61G    0.02808    0.02525   0.002697        176        640: 1\n",
      "tensor([0.89049], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00190], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.905      0.812      0.878      0.607\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      50/99      3.61G    0.02803    0.02486   0.002723        130        640: 1\n",
      "tensor([0.69146], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00190], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.89      0.834      0.883      0.608\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      51/99      3.61G    0.02776    0.02495   0.002615        178        640: 1\n",
      "tensor([0.87499], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00190], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.903      0.817      0.877      0.601\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      52/99      3.61G     0.0276    0.02456   0.002623        148        640: 1\n",
      "tensor([0.70590], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00189], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.927      0.805      0.883      0.615\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      53/99      3.61G    0.02754    0.02443   0.002574        115        640: 1\n",
      "tensor([0.65021], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00189], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.907       0.82      0.884      0.616\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      54/99      3.61G    0.02743    0.02431   0.002588        124        640: 1\n",
      "tensor([0.66224], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00189], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.924      0.803      0.872      0.605\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      55/99      3.61G    0.02712    0.02401   0.002578        163        640: 1\n",
      "tensor([0.71523], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00189], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.917      0.819      0.881      0.616\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      56/99      3.61G     0.0272    0.02433   0.002535        200        640: 1\n",
      "tensor([0.78827], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00189], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.93      0.803      0.882      0.612\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      57/99      3.61G    0.02709    0.02426   0.002525        141        640: 1\n",
      "tensor([0.67134], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00188], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.907      0.818      0.889      0.618\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      58/99      3.61G    0.02713     0.0242    0.00258        146        640: 1\n",
      "tensor([0.67436], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00188], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.921      0.813      0.887      0.622\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      59/99      3.61G    0.02688    0.02407   0.002544        168        640: 1\n",
      "tensor([0.70082], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00188], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.906       0.84      0.886      0.616\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      60/99      3.61G    0.02693    0.02386   0.002392        175        640: 1\n",
      "tensor([0.76525], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00188], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.919      0.821      0.889      0.616\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      61/99      3.61G    0.02651     0.0239   0.002469        139        640: 1\n",
      "tensor([0.73887], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00187], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.903      0.824      0.889      0.624\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      62/99      3.61G    0.02643     0.0232   0.002411        117        640: 1\n",
      "tensor([0.63360], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00187], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.924      0.821      0.875      0.613\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      63/99      3.61G     0.0266    0.02351   0.002362        129        640: 1\n",
      "tensor([0.65361], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00187], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.921      0.817      0.884      0.619\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      64/99      3.61G    0.02642    0.02367   0.002511        109        640: 1\n",
      "tensor([0.62612], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00186], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.917      0.838      0.887      0.622\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      65/99      3.61G    0.02638    0.02371   0.002338        154        640: 1\n",
      "tensor([0.77817], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00186], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.932      0.804      0.887      0.628\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      66/99      3.61G    0.02613    0.02349   0.002368        119        640: 1\n",
      "tensor([0.64940], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00186], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.933       0.82      0.884      0.621\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      67/99      3.61G    0.02609    0.02293   0.002364        153        640: 1\n",
      "tensor([0.71218], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00186], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.922      0.834      0.885      0.623\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      68/99      3.61G    0.02606    0.02347   0.002359        116        640: 1\n",
      "tensor([0.62373], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00185], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.909      0.838      0.888      0.627\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      69/99      3.61G    0.02566    0.02283   0.002365        141        640: 1\n",
      "tensor([0.67358], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00185], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.918      0.833      0.892      0.632\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      70/99      3.61G    0.02575    0.02303     0.0023        175        640: 1\n",
      "tensor([0.82253], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00185], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.91      0.824      0.892      0.628\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      71/99      3.61G     0.0259    0.02307   0.002336        161        640: 1\n",
      "tensor([0.73112], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00185], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.922       0.83      0.891      0.632\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      72/99      3.61G    0.02564    0.02268   0.002234        114        640: 1\n",
      "tensor([0.61011], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00184], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.917      0.824      0.888      0.627\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      73/99      3.61G    0.02575    0.02313   0.002298        141        640: 1\n",
      "tensor([0.69109], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00184], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.915      0.826      0.884      0.626\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      74/99      3.61G    0.02553      0.023   0.002227        133        640: 1\n",
      "tensor([0.58551], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00184], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.898      0.841      0.891      0.637\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      75/99      3.61G     0.0253    0.02254   0.002188        159        640: 1\n",
      "tensor([0.75201], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00183], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.927      0.815      0.892      0.645\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      76/99      3.61G    0.02543    0.02254   0.002259        122        640: 1\n",
      "tensor([0.54742], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00183], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.913       0.83      0.891      0.642\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      77/99      3.61G    0.02543    0.02276   0.002202        137        640: 1\n",
      "tensor([0.66619], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00183], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.916      0.831      0.891      0.641\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      78/99      3.61G    0.02531    0.02235   0.002219        137        640: 1\n",
      "tensor([0.64689], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00182], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675        0.9      0.837      0.892      0.636\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      79/99      3.61G    0.02476     0.0219   0.002253        161        640: 1\n",
      "tensor([0.73790], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00182], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.87      0.865       0.89      0.639\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      80/99      3.61G    0.02501    0.02244   0.002142        154        640: 1\n",
      "tensor([0.62455], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00182], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.902      0.851      0.897      0.647\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      81/99      3.61G    0.02488    0.02213   0.002199        181        640: 1\n",
      "tensor([0.73102], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00181], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.915      0.835       0.89      0.638\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      82/99      3.61G    0.02471    0.02197   0.002128        149        640: 1\n",
      "tensor([0.62234], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00181], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.927      0.823      0.893      0.643\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      83/99      3.61G    0.02464    0.02207    0.00207        118        640: 1\n",
      "tensor([0.60183], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00181], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.921      0.845      0.894      0.641\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      84/99      3.61G    0.02451    0.02187   0.002094        178        640: 1\n",
      "tensor([0.73942], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00181], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.908      0.848      0.891      0.637\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      85/99      3.61G    0.02444    0.02183   0.002055        140        640: 1\n",
      "tensor([0.63718], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00180], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.914      0.856      0.896      0.647\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      86/99      3.61G    0.02447     0.0219   0.002126        119        640: 1\n",
      "tensor([0.51720], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00180], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.921      0.843      0.893      0.641\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      87/99      3.61G    0.02442    0.02203   0.002043        114        640: 1\n",
      "tensor([0.50637], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00180], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.918      0.851      0.889      0.644\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      88/99      3.61G     0.0245    0.02166   0.002116        117        640: 1\n",
      "tensor([0.53587], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00180], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.922      0.849      0.893      0.644\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      89/99      3.61G    0.02432    0.02176   0.002038        118        640: 1\n",
      "tensor([0.55467], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00179], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.912      0.851      0.895       0.65\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      90/99      3.61G    0.02423    0.02146   0.001968        115        640: 1\n",
      "tensor([0.56866], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00179], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.916      0.837       0.89      0.645\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      91/99      3.61G    0.02398    0.02124   0.001994        159        640: 1\n",
      "tensor([0.73645], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00179], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.917      0.839      0.887      0.644\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      92/99      3.61G    0.02403    0.02147   0.002056        165        640: 1\n",
      "tensor([0.68350], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00179], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.913      0.835      0.889      0.646\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      93/99      3.61G    0.02405    0.02107   0.002071        195        640:  \n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      95/99      3.61G    0.02358    0.02096   0.001881        121        640: 1\n",
      "tensor([0.58210], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00178], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.892      0.856       0.89      0.647\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      96/99      3.61G    0.02387    0.02093   0.002018        195        640: 1\n",
      "tensor([0.63439], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00178], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.912      0.846      0.892      0.648\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      97/99      3.61G    0.02387    0.02107   0.001942        101        640: 1\n",
      "tensor([0.57391], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00178], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.901      0.857      0.892      0.649\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      98/99      3.61G    0.02357    0.02093      0.002        137        640: 1\n",
      "tensor([0.56886], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00178], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.902      0.855      0.895      0.653\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      99/99      3.61G    0.02359    0.02108   0.001906        115        640: 1\n",
      "tensor([0.49909], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00178], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.922      0.837      0.892      0.654\n",
      "\n",
      "100 epochs completed in 1.110 hours.\n",
      "Optimizer stripped from runs/train/exp65/weights/last.pt, 14.3MB\n",
      "Optimizer stripped from runs/train/exp65/weights/best.pt, 14.3MB\n",
      "\n",
      "Validating runs/train/exp65/weights/best.pt...\n",
      "Fusing layers... \n",
      "YOLOv5s_kitti summary: 157 layers, 7031701 parameters, 0 gradients, 15.8 GFLOPs\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.922      0.837      0.892      0.654\n",
      "                   Car       1048       4012      0.947      0.916      0.966      0.791\n",
      "                   Van       1048        431      0.929      0.917      0.961      0.784\n",
      "                 Truck       1048        166      0.929       0.94      0.959      0.777\n",
      "                  Tram       1048         56      0.907      0.911       0.95       0.73\n",
      "            Pedestrian       1048        618      0.879      0.722      0.826      0.463\n",
      "        Person_sitting       1048         20          1      0.641      0.724      0.463\n",
      "               Cyclist       1048        234      0.872      0.808      0.864      0.585\n",
      "                  Misc       1048        138      0.911      0.841      0.888      0.642\n",
      "Results saved to \u001b[1mruns/train/exp65\u001b[0m\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Comet.ml Experiment Summary\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Data:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     display_summary_level : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     name                  : exp\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     url                   : \u001b[38;5;39mhttps://www.comet.com/nagasaki-soyorin/yolov5/1e393e7602234cc89cb25c6969efd22e\u001b[0m\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Metrics [count] (min, max):\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_f1                         : 0.9315142975336288\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_false_positives            : 205.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_mAP@.5                     : 0.9657087442907524\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_mAP@.5:.95                 : 0.7907972406864735\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_precision                  : 0.947294471528492\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_recall                     : 0.9162512462612163\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_support                    : 4012\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_true_positives             : 3676.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_f1                     : 0.8388301713639507\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_false_positives        : 28.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_mAP@.5                 : 0.8635058134399302\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_mAP@.5:.95             : 0.5847842331593861\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_precision              : 0.8724651337554563\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_recall                 : 0.8076923076923077\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_support                : 234\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_true_positives         : 189.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_f1                        : 0.8743250424823747\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_false_positives           : 11.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_mAP@.5                    : 0.8883247890490062\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_mAP@.5:.95                : 0.6420757779906694\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_precision                 : 0.9108931287477329\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_recall                    : 0.8405797101449275\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_support                   : 138\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_true_positives            : 116.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_f1                  : 0.7925103873691179\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_false_positives     : 62.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_mAP@.5              : 0.8259305981283025\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_mAP@.5:.95          : 0.463392103123949\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_precision           : 0.8787531513404722\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_recall              : 0.7216828478964401\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_support             : 618\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_true_positives      : 446.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_f1              : 0.7809201376123107\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_false_positives : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_mAP@.5          : 0.724307139401479\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_mAP@.5:.95      : 0.4626773291064647\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_precision       : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_recall          : 0.640581607248274\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_support         : 20\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_true_positives  : 13.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_f1                        : 0.9088661090678181\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_false_positives           : 5.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_mAP@.5                    : 0.9504011790791503\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_mAP@.5:.95                : 0.730171590418422\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_precision                 : 0.907025418499416\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_recall                    : 0.9107142857142857\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_support                   : 56\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_true_positives            : 51.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_f1                       : 0.9344856545144432\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_false_positives          : 12.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_mAP@.5                   : 0.9592136321983521\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_mAP@.5:.95               : 0.776949966406929\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_precision                : 0.9292711249475687\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_recall                   : 0.9397590361445783\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_support                  : 166\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_true_positives           : 156.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_f1                         : 0.9231356389097299\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_false_positives            : 30.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_mAP@.5                     : 0.9611884230685368\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_mAP@.5:.95                 : 0.7840303991326075\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_precision                  : 0.9294431750877677\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_recall                     : 0.9169131360897399\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_support                    : 431\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_true_positives             : 395.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     loss [2610]                    : (0.5262854099273682, 2.1990065574645996)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/mAP_0.5 [200]          : (0.21851911021107773, 0.8965074493126798)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/mAP_0.5:0.95 [200]     : (0.11564171930905776, 0.6543387119115156)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/precision [200]        : (0.4535713817138584, 0.9332436902370141)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/recall [200]           : (0.215440243386976, 0.8648578150653652)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/box_loss [200]           : (0.023565657436847687, 0.03831126540899277)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/cls_loss [200]           : (0.0018807189771905541, 0.007072508335113525)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/obj_loss [200]           : (0.020931383594870567, 0.0343809500336647)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/box_loss [200]             : (0.024960843846201897, 0.05483492091298103)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/cls_loss [200]             : (0.0028617067728191614, 0.028329098597168922)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/obj_loss [200]             : (0.03932727500796318, 0.10985703766345978)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr0 [200]                    : (0.0002980000000000002, 0.07011450381679389)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr1 [200]                    : (0.0002980000000000002, 0.009789529262086514)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr2 [200]                    : (0.0002980000000000002, 0.009789529262086514)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Others:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Name                        : exp\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Run Path                    : nagasaki-soyorin/yolov5/1e393e7602234cc89cb25c6969efd22e\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_batch_metrics     : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_confusion_matrix  : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_per_class_metrics : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_max_image_uploads     : 100\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_mode                  : online\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_model_name            : yolov5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hasNestedParams             : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Parameters:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     anchor_t            : 4.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     artifact_alias      : latest\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     batch_size          : 16\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     box                 : 0.05\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bucket              : \n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cache               : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cls                 : 0.05000000000000001\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cls_pw              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     copy_paste          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cos_lr              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     degrees             : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     device              : \n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     entity              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     evolve              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     evolve_population   : data/hyps\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ewc_lambda          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ewc_pt              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     exist_ok            : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     fl_gamma            : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     fliplr              : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     flipud              : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     freeze              : [0]\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_h               : 0.015\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_s               : 0.7\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_v               : 0.4\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|anchor_t        : 4.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|box             : 0.05\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|cls             : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|cls_pw          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|copy_paste      : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|degrees         : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|fl_gamma        : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|fliplr          : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|flipud          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_h           : 0.015\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_s           : 0.7\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_v           : 0.4\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|iou_t           : 0.2\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|lr0             : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|lrf             : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|mixup           : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|momentum        : 0.937\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|mosaic          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|obj             : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|obj_pw          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|perspective     : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|scale           : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|shear           : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|translate       : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_bias_lr  : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_epochs   : 3.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_momentum : 0.8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|weight_decay    : 0.0005\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     image_weights       : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     imgsz               : 640\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     iou_t               : 0.2\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     label_smoothing     : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     local_rank          : -1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     lr0                 : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     lrf                 : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     mixup               : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     momentum            : 0.937\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     mosaic              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     multi_scale         : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     name                : exp\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ndjson_console      : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ndjson_file         : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noautoanchor        : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noplots             : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     nosave              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noval               : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     obj                 : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     obj_pw              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     optimizer           : SGD\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     patience            : 100\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     perspective         : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     project             : runs/train\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     quad                : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     rect                : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     resume              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     resume_evolve       : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_dir            : runs/train/exp65\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_period         : -1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     scale               : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     seed                : 0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     shear               : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     single_cls          : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sync_bn             : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     translate           : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     upload_dataset      : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val_conf_threshold  : 0.001\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val_iou_threshold   : 0.6\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_bias_lr      : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_epochs       : 3.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_momentum     : 0.8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     weight_decay        : 0.0005\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     workers             : 8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Uploads:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     asset                        : 13 (1.82 MB)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conda-environment-definition : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conda-info                   : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conda-specification          : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     confusion-matrix             : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     environment details          : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     git metadata                 : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     images                       : 106\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     installed packages           : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     model graph                  : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     os packages                  : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m \n"
     ]
    }
   ],
   "source": [
    "command = f\"\"\"\n",
    "env COMET_LOG_PER_CLASS_METRICS=true python train_SI.py \\\n",
    "--img 640 \\\n",
    "--bbox_interval 1 \\\n",
    "--cfg models/yolov5s_kitti.yaml \\\n",
    "--data data/kitti.yaml \\\n",
    "--epochs 100 \\\n",
    "--weights ./runs/train/fog_02/weights/best.pt \\\n",
    "--SI_enable \\\n",
    "--SI_pt ./runs/train/fog_02/weights/si.pt \\\n",
    "--SI_lambda 5e-3 \\\n",
    "\"\"\"\n",
    "!{command}\n",
    "# --weights ./runs/train/exp3/weights/best.pt \\\n",
    "# 0.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c5596aa1-5859-42ed-870b-d6fedccd1c07",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mval: \u001b[0mdata=data/kitti.yaml, weights=['runs/train/exp65/weights/last.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.6, max_det=300, task=test, device=, workers=8, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=False, project=runs/val, name=exp, exist_ok=False, half=False, dnn=False\n",
      "YOLOv5 🚀 68de71e8 Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA vGPU-32GB, 32260MiB)\n",
      "\n",
      "Fusing layers... \n",
      "YOLOv5s_kitti summary: 157 layers, 7031701 parameters, 0 gradients, 15.8 GFLOPs\n",
      "\u001b[34m\u001b[1mtest: \u001b[0mScanning /root/autodl-tmp/datasets/kitti/labels/test.cache... 2244 images,\u001b[0m\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       2244      12198      0.838      0.587      0.689      0.421\n",
      "                   Car       2244       8711      0.916      0.722      0.845        0.6\n",
      "                   Van       2244        861      0.841      0.632      0.723      0.495\n",
      "                 Truck       2244        333      0.871      0.591      0.674      0.415\n",
      "                  Tram       2244        138      0.818      0.565      0.674      0.352\n",
      "            Pedestrian       2244       1286      0.821      0.582      0.676      0.368\n",
      "        Person_sitting       2244         89      0.755      0.472      0.596      0.323\n",
      "               Cyclist       2244        496      0.836      0.488      0.587      0.337\n",
      "                  Misc       2244        284      0.847      0.644      0.736      0.473\n",
      "Speed: 0.1ms pre-process, 0.8ms inference, 0.9ms NMS per image at shape (32, 3, 640, 640)\n",
      "Results saved to \u001b[1mruns/val/exp72\u001b[0m\n",
      "Test set val successfully!\n"
     ]
    }
   ],
   "source": [
    "# 这是无雾训练集\n",
    "model = f'runs/train/exp65/weights/last.pt'\n",
    "\n",
    "val_command = f\" \\\n",
    "python val.py \\\n",
    "--data data/kitti.yaml \\\n",
    "--weights {model} \\\n",
    "--task test &&\\\n",
    "echo 'Test set val successfully!' \\\n",
    "\" \n",
    "!{val_command}\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8c212934-1b29-4304-9fca-eb9584478087",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "18e0650c-31db-4da2-a923-4bc116f1b151",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "abbdf061-bad2-4e31-afb5-165cdfaaf81e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set updated successfully!\n"
     ]
    }
   ],
   "source": [
    "# 替换验证集, 为了测灾难性遗忘所以用无雾验证集\n",
    "update_testsets = f\" \\\n",
    "rm ../datasets/kitti/images/val/* &&\\\n",
    "cp /root/autodl-tmp/datasets/kitti/images/origin_val/* ../datasets/kitti/images/val/ && \\\n",
    "echo 'Test set updated successfully!' \\\n",
    "\" \n",
    "!{update_testsets}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5920599a-1320-4d35-91e8-f76868868a5e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8159b717-297f-41f0-9780-afaecae88acb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "74cca6b0-a4c9-47e9-ba43-86b3d2e479b8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3c7e05c2-e29c-472f-a099-43f128565052",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "83bfb49f-6a2f-407b-8c36-638c9233e4e1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f0b0953c-4706-47af-a974-f29a4c62aac3",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mtrain_SI: \u001b[0mweights=./runs/train/fog_02/weights/best.pt, cfg=models/yolov5s_kitti.yaml, data=data/kitti.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=60, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, evolve_population=data/hyps, resume_evolve=None, bucket=, cache=None, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs/train, name=exp, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=1, artifact_alias=latest, ndjson_console=False, ndjson_file=False, ewc_pt=None, ewc_lambda=0.0, SI_enable=True, SI_pt=./runs/train/fog_02/weights/si.pt, SI_lambda=0.05\n",
      "\u001b[34m\u001b[1mgithub: \u001b[0m⚠️ YOLOv5 is out of date by 2882 commits. Use 'git pull ultralytics master' or 'git clone https://github.com/ultralytics/yolov5' to update.\n",
      "YOLOv5 🚀 68de71e8 Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA vGPU-32GB, 32260MiB)\n",
      "\n",
      "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0\n",
      "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Experiment is live on comet.com \u001b[38;5;39mhttps://www.comet.com/nagasaki-soyorin/yolov5/c8353279c5f749cf86f25d290eb2cad5\u001b[0m\n",
      "\n",
      "\n",
      "                 from  n    params  module                                  arguments                     \n",
      "  0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]              \n",
      "  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                \n",
      "  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   \n",
      "  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
      "  4                -1  2    115712  models.common.C3                        [128, 128, 2]                 \n",
      "  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              \n",
      "  6                -1  3    625152  models.common.C3                        [256, 256, 3]                 \n",
      "  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              \n",
      "  8                -1  1   1182720  models.common.C3                        [512, 512, 1]                 \n",
      "  9                -1  1    656896  models.common.SPPF                      [512, 512, 5]                 \n",
      " 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
      " 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 12           [-1, 6]  1         0  models.common.Concat                    [1]                           \n",
      " 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          \n",
      " 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
      " 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 16           [-1, 4]  1         0  models.common.Concat                    [1]                           \n",
      " 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          \n",
      " 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              \n",
      " 19          [-1, 14]  1         0  models.common.Concat                    [1]                           \n",
      " 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          \n",
      " 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n",
      " 22          [-1, 10]  1         0  models.common.Concat                    [1]                           \n",
      " 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          \n",
      " 24      [17, 20, 23]  1     35061  models.yolo.Detect                      [8, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
      "YOLOv5s_kitti summary: 214 layers, 7041205 parameters, 7041205 gradients, 16.0 GFLOPs\n",
      "\n",
      "Transferred 348/349 items from runs/train/fog_02/weights/best.pt\n",
      "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
      "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 60 weight(decay=0.0005), 60 bias\n",
      "\u001b[34m\u001b[1malbumentations: \u001b[0m1 validation error for InitSchema\n",
      "size\n",
      "  Field required [type=missing, input_value={'height': 640, 'width': ...'mask_interpolation': 0}, input_type=dict]\n",
      "    For further information visit https://errors.pydantic.dev/2.10/v/missing\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /root/autodl-tmp/datasets/kitti/labels/train.cache... 4189 image\u001b[0m\n",
      "\u001b[34m\u001b[1mval: \u001b[0mScanning /root/autodl-tmp/datasets/kitti/labels/val.cache... 1048 images, 0\u001b[0m\n",
      "\n",
      "\u001b[34m\u001b[1mAutoAnchor: \u001b[0m4.81 anchors/target, 0.999 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅\n",
      "Plotting labels to runs/train/exp54/labels.jpg... \n",
      "Image sizes 640 train, 640 val\n",
      "Using 8 dataloader workers\n",
      "Logging results to \u001b[1mruns/train/exp54\u001b[0m\n",
      "Starting training for 60 epochs...\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       0/59      3.61G    0.02801    0.02536   0.002652        182        640:  \u001b[1;38;5;214mCOMET WARNING:\u001b[0m Unknown error retrieving Conda package as an explicit file\n",
      "       0/59      3.61G    0.02806    0.02525   0.002643        128        640: 1\n",
      "tensor([0.72943], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00178], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.869      0.606       0.71      0.442\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       1/59      3.61G    0.03085    0.02503   0.002621        133        640: 1\n",
      "tensor([0.82659], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00218], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.883      0.712      0.796      0.488\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       2/59      3.61G     0.0336    0.02693   0.003171        131        640: 1\n",
      "tensor([0.81911], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00108], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.877      0.669      0.769      0.464\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       3/59      3.61G    0.03417    0.02834    0.00375        108        640: 1\n",
      "tensor([0.77709], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00110], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.877      0.697       0.78      0.475\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       4/59      3.61G    0.03396    0.02831   0.003942        156        640: 1\n",
      "tensor([0.83891], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00176], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.814      0.688      0.755      0.463\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       5/59      3.61G    0.03338    0.02836   0.004024        123        640: 1\n",
      "tensor([0.82599], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00230], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.861      0.672      0.781      0.481\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       6/59      3.61G    0.03285      0.028   0.003826        174        640: 1\n",
      "tensor([0.92593], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00272], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.826      0.712      0.798      0.486\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       7/59      3.61G    0.03265    0.02751   0.003805        166        640: 1\n",
      "tensor([1.00400], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00306], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.868      0.699      0.788      0.506\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       8/59      3.61G    0.03183    0.02764   0.003632        152        640: 1\n",
      "tensor([0.84953], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00329], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.876      0.715      0.807        0.5\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       9/59      3.61G    0.03207     0.0275   0.003682        136        640: 1\n",
      "tensor([0.83354], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00347], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.871      0.738      0.806       0.51\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      10/59      3.61G    0.03173    0.02734    0.00354        134        640: 1\n",
      "tensor([0.75620], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00365], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.866      0.725      0.814      0.523\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      11/59      3.61G    0.03112    0.02669   0.003463        182        640: 1\n",
      "tensor([0.87562], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00377], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.846      0.728      0.799      0.514\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      12/59      3.61G    0.03105    0.02693   0.003388        128        640: 1\n",
      "tensor([0.69986], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00388], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.878      0.757      0.826      0.533\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      13/59      3.61G    0.03055    0.02634   0.003264        112        640: 1\n",
      "tensor([0.80414], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00397], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.872      0.751      0.837      0.529\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      14/59      3.61G    0.03032    0.02652   0.003247        151        640: 1\n",
      "tensor([0.77765], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00408], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.896      0.733      0.816      0.521\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      15/59      3.61G    0.03023    0.02644   0.003262        132        640: 1\n",
      "tensor([0.76155], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00415], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.865      0.721      0.796      0.512\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      16/59      3.61G    0.02994    0.02612   0.003229        131        640: 1\n",
      "tensor([0.75308], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00424], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.86      0.775      0.842       0.54\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      17/59      3.61G    0.02986    0.02594   0.003231        159        640: 1\n",
      "tensor([0.87270], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00431], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.874      0.744      0.819      0.532\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      18/59      3.61G     0.0296    0.02566   0.003119        125        640: 1\n",
      "tensor([0.67017], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00434], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.896      0.753      0.841      0.551\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      19/59      3.61G     0.0295    0.02592   0.003129         88        640: 1\n",
      "tensor([0.57003], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00440], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.886      0.766      0.845      0.554\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      20/59      3.61G    0.02896    0.02504   0.002984        137        640: 1\n",
      "tensor([0.84228], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00442], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.92      0.744       0.84      0.542\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      21/59      3.61G    0.02884    0.02559   0.002963        166        640: 1\n",
      "tensor([0.76912], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00446], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.92      0.731      0.826      0.543\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      22/59      3.61G    0.02868    0.02521   0.002884        161        640: 1\n",
      "tensor([0.79172], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00450], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.891      0.734       0.81      0.528\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      23/59      3.61G    0.02835    0.02474   0.002886        118        640: 1\n",
      "tensor([0.67385], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00451], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.897      0.799      0.869      0.583\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      24/59      3.61G    0.02798    0.02473   0.002815        151        640: 1\n",
      "tensor([0.77573], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00451], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.907      0.761      0.851      0.568\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      25/59      3.61G    0.02815    0.02492   0.002791        133        640: 1\n",
      "tensor([0.70515], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00454], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.926      0.739      0.842      0.562\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      26/59      3.61G    0.02782    0.02448    0.00265        154        640: 1\n",
      "tensor([0.82054], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00457], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.912       0.78      0.858      0.575\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      27/59      3.61G    0.02777    0.02465   0.002863        122        640: 1\n",
      "tensor([0.68650], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00456], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.93       0.76      0.846      0.555\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      28/59      3.61G    0.02742    0.02436   0.002657        123        640: 1\n",
      "tensor([0.59656], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00456], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.912      0.778      0.845      0.566\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      29/59      3.61G    0.02737    0.02402   0.002609        127        640: 1\n",
      "tensor([0.60889], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00456], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.884      0.758      0.843      0.569\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      30/59      3.61G    0.02699    0.02323   0.002567        127        640: 1\n",
      "tensor([0.64046], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00456], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.877      0.775      0.846      0.569\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      31/59      3.61G     0.0269     0.0237   0.002575        122        640: 1\n",
      "tensor([0.70488], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00456], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.895      0.771      0.841       0.56\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      32/59      3.61G      0.027    0.02394   0.002566        146        640: 1\n",
      "tensor([0.74217], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00456], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.897      0.773      0.849       0.56\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      33/59      3.61G    0.02662    0.02333   0.002493        202        640: 1\n",
      "tensor([0.80165], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00455], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.921      0.762      0.847      0.578\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      34/59      3.61G    0.02639    0.02315   0.002477         94        640: 1\n",
      "tensor([0.55582], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00454], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.839      0.782      0.843       0.57\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      35/59      3.61G    0.02627    0.02315   0.002424        152        640: 1\n",
      "tensor([0.76467], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00453], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.89      0.802      0.852      0.579\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      36/59      3.61G    0.02596    0.02289   0.002444        123        640: 1\n",
      "tensor([0.61362], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00452], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.918      0.764      0.849      0.579\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      37/59      3.61G    0.02584    0.02299   0.002433        162        640: 1\n",
      "tensor([0.66063], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00451], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.912       0.77      0.843      0.569\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      38/59      3.61G    0.02591    0.02318   0.002395        161        640: 1\n",
      "tensor([0.67836], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00450], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.901      0.782      0.844      0.579\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      39/59      3.61G    0.02557    0.02286   0.002372        122        640: 1\n",
      "tensor([0.62288], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00449], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.913      0.765      0.848      0.575\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      40/59      3.61G    0.02555     0.0225   0.002354        126        640: 1\n",
      "tensor([0.58915], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00448], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.92      0.752      0.845      0.581\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      41/59      3.61G    0.02518    0.02249   0.002354         90        640: 1\n",
      "tensor([0.55547], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00447], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.897      0.797      0.858      0.586\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      42/59      3.61G    0.02503    0.02229   0.002203        118        640: 1\n",
      "tensor([0.63354], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00446], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675        0.9      0.775      0.855      0.582\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      43/59      3.61G    0.02495    0.02227   0.002122        157        640: 1\n",
      "tensor([0.69585], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00445], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.897      0.779      0.851      0.588\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      44/59      3.61G    0.02474    0.02212   0.002177        104        640: 1\n",
      "tensor([0.48454], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00445], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.892      0.791      0.857       0.59\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      45/59      3.61G    0.02489    0.02245   0.002092        157        640: 1\n",
      "tensor([0.64189], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00443], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.904      0.746      0.844      0.574\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      46/59      3.61G    0.02464    0.02176   0.002081        108        640: 1\n",
      "tensor([0.48279], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00442], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.891      0.798      0.859      0.587\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      47/59      3.61G    0.02452    0.02171   0.002153        159        640: 1\n",
      "tensor([0.64780], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00441], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "      49/59      3.61G    0.02434    0.02197   0.002103        176        640: 1\n",
      "tensor([0.77512], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00438], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.925      0.766      0.855      0.593\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      50/59      3.61G    0.02427    0.02145   0.002096        130        640: 1\n",
      "tensor([0.57695], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00437], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.909      0.789      0.847      0.586\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      51/59      3.61G    0.02393    0.02159    0.00201        178        640: 1\n",
      "tensor([0.73680], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00436], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.905      0.777      0.844       0.59\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      52/59      3.61G    0.02383    0.02123   0.002003        148        640: 1\n",
      "tensor([0.61518], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00435], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.906      0.793      0.861      0.598\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      53/59      3.61G     0.0237    0.02108   0.001951        115        640: 1\n",
      "tensor([0.57193], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00434], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.91       0.77      0.849      0.592\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      54/59      3.61G     0.0237    0.02094      0.002        124        640: 1\n",
      "tensor([0.51790], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00433], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.903      0.772      0.851      0.598\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      55/59      3.61G    0.02344    0.02063   0.001985        163        640: 1\n",
      "tensor([0.61867], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00432], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.907      0.769      0.848      0.594\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      56/59      3.61G    0.02335    0.02095   0.001942        200        640: 1\n",
      "tensor([0.66552], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00431], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.904      0.771      0.851      0.596\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      57/59      3.61G    0.02337    0.02085   0.001946        141        640: 1\n",
      "tensor([0.60868], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00431], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.884      0.781       0.85      0.596\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      58/59      3.61G    0.02343    0.02082   0.001997        146        640: 1\n",
      "tensor([0.58649], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00430], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.918      0.773      0.853      0.598\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      59/59      3.61G    0.02326    0.02066   0.001946        168        640: 1\n",
      "tensor([0.64176], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00430], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.897      0.786      0.855      0.603\n",
      "\n",
      "60 epochs completed in 1.068 hours.\n",
      "Optimizer stripped from runs/train/exp54/weights/last.pt, 14.3MB\n",
      "Optimizer stripped from runs/train/exp54/weights/best.pt, 14.3MB\n",
      "\n",
      "Validating runs/train/exp54/weights/best.pt...\n",
      "Fusing layers... \n",
      "YOLOv5s_kitti summary: 157 layers, 7031701 parameters, 0 gradients, 15.8 GFLOPs\n",
      "                 Class     Images  Instances          P          R      mAP50   ^C\n",
      "                 Class     Images  Instances          P          R      mAP50   \n"
     ]
    }
   ],
   "source": [
    "command = f\"\"\"\n",
    "env COMET_LOG_PER_CLASS_METRICS=true python train_SI.py \\\n",
    "--img 640 \\\n",
    "--bbox_interval 1 \\\n",
    "--cfg models/yolov5s_kitti.yaml \\\n",
    "--data data/kitti.yaml \\\n",
    "--epochs 60 \\\n",
    "--weights ./runs/train/fog_02/weights/best.pt \\\n",
    "--SI_enable \\\n",
    "--SI_pt ./runs/train/fog_02/weights/si.pt \\\n",
    "--SI_lambda 5e-2 \\\n",
    "\"\"\"\n",
    "!{command}\n",
    "# --weights ./runs/train/exp3/weights/best.pt \\\n",
    "# 这个吃完饭回来后再看。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b31a9453-ce4a-4d4b-8f87-2b6c0e1b2e2a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c3739c4e-1da0-4f3c-9c65-b785c0a41953",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8421ad52-0311-4a91-8b0a-bc635178120c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "80c32ba4-47db-4316-97af-f7ac708aeb6c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "83918dec-f496-4df0-a562-bf92d1e864fc",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mtrain_SI: \u001b[0mweights=./runs/train/fog_02/weights/best.pt, cfg=models/yolov5s_kitti.yaml, data=data/kitti.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=60, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, evolve_population=data/hyps, resume_evolve=None, bucket=, cache=None, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs/train, name=exp, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=1, artifact_alias=latest, ndjson_console=False, ndjson_file=False, ewc_pt=None, ewc_lambda=0.0, SI_enable=True, SI_pt=./runs/train/fog_02/weights/si.pt, SI_lambda=0.001\n",
      "Command 'git fetch ultralytics' timed out after 5 seconds\n",
      "YOLOv5 🚀 68de71e8 Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA vGPU-32GB, 32260MiB)\n",
      "\n",
      "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0\n",
      "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Experiment is live on comet.com \u001b[38;5;39mhttps://www.comet.com/nagasaki-soyorin/yolov5/6a1fc979c85741769a1729d0c262a377\u001b[0m\n",
      "\n",
      "\n",
      "                 from  n    params  module                                  arguments                     \n",
      "  0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]              \n",
      "  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                \n",
      "  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   \n",
      "  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
      "  4                -1  2    115712  models.common.C3                        [128, 128, 2]                 \n",
      "  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              \n",
      "  6                -1  3    625152  models.common.C3                        [256, 256, 3]                 \n",
      "  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              \n",
      "  8                -1  1   1182720  models.common.C3                        [512, 512, 1]                 \n",
      "  9                -1  1    656896  models.common.SPPF                      [512, 512, 5]                 \n",
      " 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
      " 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 12           [-1, 6]  1         0  models.common.Concat                    [1]                           \n",
      " 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          \n",
      " 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
      " 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 16           [-1, 4]  1         0  models.common.Concat                    [1]                           \n",
      " 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          \n",
      " 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              \n",
      " 19          [-1, 14]  1         0  models.common.Concat                    [1]                           \n",
      " 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          \n",
      " 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n",
      " 22          [-1, 10]  1         0  models.common.Concat                    [1]                           \n",
      " 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          \n",
      " 24      [17, 20, 23]  1     35061  models.yolo.Detect                      [8, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
      "YOLOv5s_kitti summary: 214 layers, 7041205 parameters, 7041205 gradients, 16.0 GFLOPs\n",
      "\n",
      "Transferred 348/349 items from runs/train/fog_02/weights/best.pt\n",
      "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
      "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 60 weight(decay=0.0005), 60 bias\n",
      "\u001b[34m\u001b[1malbumentations: \u001b[0m1 validation error for InitSchema\n",
      "size\n",
      "  Field required [type=missing, input_value={'height': 640, 'width': ...'mask_interpolation': 0}, input_type=dict]\n",
      "    For further information visit https://errors.pydantic.dev/2.10/v/missing\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /root/autodl-tmp/datasets/kitti/labels/train.cache... 4189 image\u001b[0m\n",
      "\u001b[34m\u001b[1mval: \u001b[0mScanning /root/autodl-tmp/datasets/kitti/labels/val.cache... 1048 images, 0\u001b[0m\n",
      "\n",
      "\u001b[34m\u001b[1mAutoAnchor: \u001b[0m4.81 anchors/target, 0.999 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅\n",
      "Plotting labels to runs/train/exp51/labels.jpg... \n",
      "Image sizes 640 train, 640 val\n",
      "Using 8 dataloader workers\n",
      "Logging results to \u001b[1mruns/train/exp51\u001b[0m\n",
      "Starting training for 60 epochs...\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       0/59      3.55G    0.03866    0.05672   0.009854        196        640:  error: RPC failed; curl 16 Error in the HTTP2 framing layer\n",
      "fatal: expected flush after ref listing\n",
      "       0/59      3.61G    0.03053    0.02902   0.003867        128        640: 1\n",
      "tensor([0.76989], device='cuda:0', grad_fn=<AddBackward0>) tensor([2.59439e-05], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.749      0.512       0.58      0.341\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       1/59      3.61G    0.03133    0.02707    0.00332        133        640: 1\n",
      "tensor([0.83064], device='cuda:0', grad_fn=<AddBackward0>) tensor([3.31295e-05], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.816      0.587      0.666      0.397\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       2/59      3.61G    0.03374    0.02921   0.004148        131        640: 1\n",
      "tensor([0.83628], device='cuda:0', grad_fn=<AddBackward0>) tensor([2.92543e-05], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.795      0.643      0.711      0.429\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       3/59      3.61G    0.03535     0.0308   0.004838        108        640: 1\n",
      "tensor([0.80867], device='cuda:0', grad_fn=<AddBackward0>) tensor([5.14572e-05], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.752      0.525      0.616      0.361\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       4/59      3.61G    0.03545    0.03048    0.00478        156        640: 1\n",
      "tensor([0.93047], device='cuda:0', grad_fn=<AddBackward0>) tensor([8.39297e-05], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.778       0.66       0.75      0.443\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       5/59      3.61G    0.03483       0.03   0.004653        123        640: 1\n",
      "tensor([0.83240], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00011], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.777      0.639      0.702      0.413\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       6/59      3.61G    0.03407    0.02912     0.0042        174        640: 1\n",
      "tensor([0.94522], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00012], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.821      0.675      0.767      0.465\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       7/59      3.61G     0.0336    0.02852   0.004111        166        640: 1\n",
      "tensor([1.05243], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00014], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.812      0.661      0.742      0.437\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       8/59      3.61G    0.03305    0.02877   0.003942        152        640: 1\n",
      "tensor([0.89020], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00015], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.846      0.649      0.735      0.445\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       9/59      3.61G    0.03266    0.02854   0.004025        136        640: 1\n",
      "tensor([0.84389], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00016], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.828      0.653      0.743      0.439\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      10/59      3.61G    0.03235    0.02824   0.003814        134        640: 1\n",
      "tensor([0.78881], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00017], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.845      0.689      0.766      0.466\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      11/59      3.61G    0.03192    0.02759   0.003673        182        640: 1\n",
      "tensor([0.89330], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00018], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.842      0.688      0.778      0.475\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      12/59      3.61G    0.03173    0.02785   0.003651        128        640: 1\n",
      "tensor([0.72777], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00019], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.826      0.663      0.768      0.465\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      13/59      3.61G    0.03124    0.02712   0.003394        112        640: 1\n",
      "tensor([0.83564], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00019], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.822      0.707      0.767       0.47\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      14/59      3.61G    0.03104    0.02723   0.003403        151        640: 1\n",
      "tensor([0.77042], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00020], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.895      0.694      0.796      0.494\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      15/59      3.61G    0.03085    0.02717   0.003338        132        640: 1\n",
      "tensor([0.78346], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00020], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.806      0.697      0.759      0.475\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      16/59      3.61G    0.03046    0.02669   0.003381        131        640: 1\n",
      "tensor([0.74798], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00021], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.871      0.676      0.795      0.486\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      17/59      3.61G     0.0307    0.02673   0.003416        159        640: 1\n",
      "tensor([0.89174], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00021], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.863      0.716        0.8      0.497\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      18/59      3.61G    0.03008    0.02638   0.003241        125        640: 1\n",
      "tensor([0.67813], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00022], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.853      0.677      0.771      0.464\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      19/59      3.61G     0.0301    0.02669   0.003284         88        640: 1\n",
      "tensor([0.62614], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00022], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.865      0.689      0.796      0.507\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      20/59      3.61G    0.02948    0.02577    0.00323        137        640: 1\n",
      "tensor([0.86317], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00022], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.868      0.688      0.789      0.508\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      21/59      3.61G    0.02947    0.02633   0.003092        166        640: 1\n",
      "tensor([0.84614], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00023], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.893      0.699      0.791      0.502\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      22/59      3.61G    0.02926    0.02591   0.003008        161        640: 1\n",
      "tensor([0.79625], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00023], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.888      0.712      0.801      0.492\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      23/59      3.61G    0.02915    0.02547   0.003015        118        640: 1\n",
      "tensor([0.67587], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00023], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.878      0.739       0.82      0.517\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      24/59      3.61G    0.02876    0.02542   0.002951        151        640: 1\n",
      "tensor([0.79796], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00023], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.868      0.713      0.803      0.511\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      25/59      3.61G     0.0286    0.02548   0.002867        133        640: 1\n",
      "tensor([0.72337], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00023], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.884      0.728      0.808      0.519\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      26/59      3.61G    0.02822    0.02501   0.002759        154        640: 1\n",
      "tensor([0.84451], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00024], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.895      0.733      0.817      0.515\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      27/59      3.61G    0.02832    0.02525   0.002987        122        640: 1\n",
      "tensor([0.70800], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00024], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.862      0.711      0.792      0.504\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      28/59      3.61G    0.02806    0.02499   0.002755        123        640: 1\n",
      "tensor([0.59071], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00024], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.861      0.733      0.808      0.513\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      29/59      3.61G     0.0279    0.02469   0.002716        127        640: 1\n",
      "tensor([0.63045], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00024], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.902      0.704      0.805      0.507\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      30/59      3.61G    0.02749    0.02378   0.002682        127        640: 1\n",
      "tensor([0.62494], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00024], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.92      0.718      0.814      0.522\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      31/59      3.61G    0.02757    0.02433   0.002701        122        640: 1\n",
      "tensor([0.69300], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00024], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675        0.9      0.706      0.804      0.511\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      32/59      3.61G    0.02738     0.0245   0.002634        146        640: 1\n",
      "tensor([0.74411], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00024], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.899       0.68      0.778      0.493\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      33/59      3.61G     0.0272    0.02388   0.002544        188        640:  "
     ]
    }
   ],
   "source": [
    "command = f\"\"\"\n",
    "env COMET_LOG_PER_CLASS_METRICS=true python train_SI.py \\\n",
    "--img 640 \\\n",
    "--bbox_interval 1 \\\n",
    "--cfg models/yolov5s_kitti.yaml \\\n",
    "--data data/kitti.yaml \\\n",
    "--epochs 60 \\\n",
    "--weights ./runs/train/fog_02/weights/best.pt \\\n",
    "--SI_enable \\\n",
    "--SI_pt ./runs/train/fog_02/weights/si.pt \\\n",
    "--SI_lambda 1e-3 \\\n",
    "\"\"\"\n",
    "!{command}\n",
    "# 0.9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "da6a5610-62f8-4c98-9c4d-2fb7c8892514",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "58da65bd-17e3-48b7-aef2-c61d98b6098b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4a8f9890-9472-4695-bf06-d4463dbaa27b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ac03aac7-0983-43f5-a4d1-a1e332756495",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mtrain_SI: \u001b[0mweights=./runs/train/fog_02/weights/best.pt, cfg=models/yolov5s_kitti.yaml, data=data/kitti.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=100, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, evolve_population=data/hyps, resume_evolve=None, bucket=, cache=None, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs/train, name=exp, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=1, artifact_alias=latest, ndjson_console=False, ndjson_file=False, ewc_pt=None, ewc_lambda=0.0, SI_enable=True, SI_pt=./runs/train/fog_02/weights/si.pt, SI_lambda=0.5\n",
      "\u001b[34m\u001b[1mgithub: \u001b[0m⚠️ YOLOv5 is out of date by 2882 commits. Use 'git pull ultralytics master' or 'git clone https://github.com/ultralytics/yolov5' to update.\n",
      "YOLOv5 🚀 68de71e8 Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA vGPU-32GB, 32260MiB)\n",
      "\n",
      "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0\n",
      "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Experiment is live on comet.com \u001b[38;5;39mhttps://www.comet.com/nagasaki-soyorin/yolov5/3ead75f8673647f5a41e658742698eb4\u001b[0m\n",
      "\n",
      "\n",
      "                 from  n    params  module                                  arguments                     \n",
      "  0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]              \n",
      "  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                \n",
      "  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   \n",
      "  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
      "  4                -1  2    115712  models.common.C3                        [128, 128, 2]                 \n",
      "  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              \n",
      "  6                -1  3    625152  models.common.C3                        [256, 256, 3]                 \n",
      "  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              \n",
      "  8                -1  1   1182720  models.common.C3                        [512, 512, 1]                 \n",
      "  9                -1  1    656896  models.common.SPPF                      [512, 512, 5]                 \n",
      " 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
      " 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 12           [-1, 6]  1         0  models.common.Concat                    [1]                           \n",
      " 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          \n",
      " 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
      " 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 16           [-1, 4]  1         0  models.common.Concat                    [1]                           \n",
      " 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          \n",
      " 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              \n",
      " 19          [-1, 14]  1         0  models.common.Concat                    [1]                           \n",
      " 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          \n",
      " 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n",
      " 22          [-1, 10]  1         0  models.common.Concat                    [1]                           \n",
      " 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          \n",
      " 24      [17, 20, 23]  1     35061  models.yolo.Detect                      [8, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
      "YOLOv5s_kitti summary: 214 layers, 7041205 parameters, 7041205 gradients, 16.0 GFLOPs\n",
      "\n",
      "Transferred 348/349 items from runs/train/fog_02/weights/best.pt\n",
      "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
      "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 60 weight(decay=0.0005), 60 bias\n",
      "\u001b[34m\u001b[1malbumentations: \u001b[0m1 validation error for InitSchema\n",
      "size\n",
      "  Field required [type=missing, input_value={'height': 640, 'width': ...'mask_interpolation': 0}, input_type=dict]\n",
      "    For further information visit https://errors.pydantic.dev/2.10/v/missing\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /root/autodl-tmp/datasets/kitti/labels/train.cache... 4189 image\u001b[0m\n",
      "\u001b[34m\u001b[1mval: \u001b[0mScanning /root/autodl-tmp/datasets/kitti/labels/val.cache... 1048 images, 0\u001b[0m\n",
      "\n",
      "\u001b[34m\u001b[1mAutoAnchor: \u001b[0m4.81 anchors/target, 0.999 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅\n",
      "Plotting labels to runs/train/exp64/labels.jpg... \n",
      "Image sizes 640 train, 640 val\n",
      "Using 8 dataloader workers\n",
      "Logging results to \u001b[1mruns/train/exp64\u001b[0m\n",
      "Starting training for 100 epochs...\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       0/99      3.61G    0.04042     0.0348   0.006866        185        640:  \u001b[1;38;5;214mCOMET WARNING:\u001b[0m Unknown error retrieving Conda package as an explicit file\n",
      "       0/99      3.61G     0.0404    0.03471   0.006849        128        640: 1\n",
      "tensor([1.61370], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.47058], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.562      0.374      0.404      0.238\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       1/99      3.61G    0.03808    0.03089   0.005248        133        640: 1\n",
      "tensor([1.39869], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.34711], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.755      0.485      0.566       0.32\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       2/99      3.61G    0.03733    0.03304   0.006406        131        640: 1\n",
      "tensor([1.10865], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.04982], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.661      0.366      0.428      0.231\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       3/99      3.61G    0.03867    0.03461   0.007123        108        640: 1\n",
      "tensor([0.88271], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.03114], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.609      0.503      0.531      0.289\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       4/99      3.61G    0.03841    0.03428   0.006946        156        640: 1\n",
      "tensor([1.07782], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.04567], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.66      0.472      0.522      0.278\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       5/99      3.61G    0.03799    0.03331   0.006297        123        640: 1\n",
      "tensor([0.94772], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.04974], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.695      0.445      0.494      0.265\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       6/99      3.61G    0.03716    0.03205   0.005514        174        640: 1\n",
      "tensor([1.09662], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.04898], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.762      0.493      0.579      0.318\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       7/99      3.61G    0.03657    0.03119   0.005303        166        640: 1\n",
      "tensor([1.15534], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.04718], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.808      0.556      0.653      0.365\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       8/99      3.61G    0.03584    0.03122   0.004977        152        640: 1\n",
      "tensor([0.99503], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.04688], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.674      0.519      0.576      0.302\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       9/99      3.61G    0.03577    0.03107   0.004976        136        640: 1\n",
      "tensor([0.94017], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.04674], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.687      0.562      0.625      0.358\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      10/99      3.61G    0.03552    0.03121   0.005122        134        640: 1\n",
      "tensor([0.92070], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.04854], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.685      0.333       0.38      0.216\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      11/99      3.61G    0.03556    0.03133   0.004996        164        640:  ^C\n",
      "      11/99      3.61G    0.03556    0.03133   0.004996        164        640:  \n"
     ]
    }
   ],
   "source": [
    "command = f\"\"\"\n",
    "env COMET_LOG_PER_CLASS_METRICS=true python train_SI.py \\\n",
    "--img 640 \\\n",
    "--bbox_interval 1 \\\n",
    "--cfg models/yolov5s_kitti.yaml \\\n",
    "--data data/kitti.yaml \\\n",
    "--epochs 100 \\\n",
    "--weights ./runs/train/fog_02/weights/best.pt \\\n",
    "--SI_enable \\\n",
    "--SI_pt ./runs/train/fog_02/weights/si.pt \\\n",
    "--SI_lambda 5e-1 \\\n",
    "\"\"\"\n",
    "!{command}\n",
    "# --weights ./runs/train/exp3/weights/best.pt \\\n",
    "# 这个吃完饭回来后再看。\n",
    "# 这个目前是1.0强度增量的最好参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e4c47b75-ae63-437f-84cf-32bfb9950d9c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set updated successfully!\n"
     ]
    }
   ],
   "source": [
    "#这个是使用ewc的增量训练在没有加雾的第一个数据集上的效果\n",
    "\n",
    "update_testsets = f\" \\\n",
    "rm ../datasets/kitti/images/test/* &&\\\n",
    "cp /root/autodl-tmp/testing/image_2/* ../datasets/kitti/images/test/ && \\\n",
    "echo 'Test set updated successfully!' \\\n",
    "\" \n",
    "!{update_testsets}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7a865118-6fd9-45b3-bc66-d625d84d5bc0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mval: \u001b[0mdata=data/kitti.yaml, weights=['runs/train/exp60/weights/last.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.6, max_det=300, task=test, device=, workers=8, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=False, project=runs/val, name=exp, exist_ok=False, half=False, dnn=False\n",
      "YOLOv5 🚀 68de71e8 Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA vGPU-32GB, 32260MiB)\n",
      "\n",
      "Fusing layers... \n",
      "YOLOv5s_kitti summary: 157 layers, 7031701 parameters, 0 gradients, 15.8 GFLOPs\n",
      "\u001b[34m\u001b[1mtest: \u001b[0mScanning /root/autodl-tmp/datasets/kitti/labels/test... 2244 images, 0 bac\u001b[0m\n",
      "\u001b[34m\u001b[1mtest: \u001b[0mNew cache created: /root/autodl-tmp/datasets/kitti/labels/test.cache\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       2244      12198       0.84      0.622      0.728      0.453\n",
      "                   Car       2244       8711      0.909      0.759       0.88      0.635\n",
      "                   Van       2244        861       0.82       0.64      0.738       0.51\n",
      "                 Truck       2244        333      0.919      0.766       0.85      0.625\n",
      "                  Tram       2244        138      0.901      0.657      0.806      0.457\n",
      "            Pedestrian       2244       1286      0.783      0.588      0.668      0.352\n",
      "        Person_sitting       2244         89      0.668      0.584      0.675      0.335\n",
      "               Cyclist       2244        496       0.91       0.45      0.563      0.314\n",
      "                  Misc       2244        284      0.811      0.535      0.647      0.396\n",
      "Speed: 0.0ms pre-process, 1.2ms inference, 1.2ms NMS per image at shape (32, 3, 640, 640)\n",
      "Results saved to \u001b[1mruns/val/exp61\u001b[0m\n",
      "Test set val successfully!\n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "开始测baseline\n",
    "首先是基于无雾数据集的测试集\n",
    "'''\n",
    "# 这是无雾训练集\n",
    "model = f'runs/train/exp60/weights/last.pt'\n",
    "\n",
    "val_command = f\" \\\n",
    "python val.py \\\n",
    "--data data/kitti.yaml \\\n",
    "--weights {model} \\\n",
    "--task test &&\\\n",
    "echo 'Test set val successfully!' \\\n",
    "\" \n",
    "!{val_command}\n",
    "\n",
    "# ewc强度0.5\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bf14de39-0c9e-48e7-859a-c0b8e1667406",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mval: \u001b[0mdata=data/kitti.yaml, weights=['runs/train/exp59/weights/last.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.6, max_det=300, task=test, device=, workers=8, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=False, project=runs/val, name=exp, exist_ok=False, half=False, dnn=False\n",
      "YOLOv5 🚀 68de71e8 Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA vGPU-32GB, 32260MiB)\n",
      "\n",
      "Fusing layers... \n",
      "YOLOv5s_kitti summary: 157 layers, 7031701 parameters, 0 gradients, 15.8 GFLOPs\n",
      "\u001b[34m\u001b[1mtest: \u001b[0mScanning /root/autodl-tmp/datasets/kitti/labels/test.cache... 2244 images,\u001b[0m\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       2244      12198      0.845      0.623      0.732      0.454\n",
      "                   Car       2244       8711      0.914      0.763      0.879       0.63\n",
      "                   Van       2244        861      0.824      0.635      0.752      0.512\n",
      "                 Truck       2244        333       0.96      0.733      0.824      0.609\n",
      "                  Tram       2244        138      0.953      0.591      0.793      0.447\n",
      "            Pedestrian       2244       1286      0.739      0.617      0.685      0.358\n",
      "        Person_sitting       2244         89      0.674      0.596       0.66      0.328\n",
      "               Cyclist       2244        496      0.878      0.463      0.584      0.329\n",
      "                  Misc       2244        284      0.815      0.589      0.679      0.418\n",
      "Speed: 0.0ms pre-process, 1.2ms inference, 1.3ms NMS per image at shape (32, 3, 640, 640)\n",
      "Results saved to \u001b[1mruns/val/exp62\u001b[0m\n",
      "Test set val successfully!\n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "开始测baseline\n",
    "首先是基于无雾数据集的测试集\n",
    "'''\n",
    "# 这是无雾训练集\n",
    "model = f'runs/train/exp59/weights/last.pt'\n",
    "\n",
    "val_command = f\" \\\n",
    "python val.py \\\n",
    "--data data/kitti.yaml \\\n",
    "--weights {model} \\\n",
    "--task test &&\\\n",
    "echo 'Test set val successfully!' \\\n",
    "\" \n",
    "!{val_command}\n",
    "\n",
    "# ewc强度0.5\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f6af7366-cdc8-44b1-872b-2a88b4b7e247",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "16d0e733-8be8-4f3d-bdd0-ca8816104c4d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2cf7f14c-16bb-47d7-8234-d1ca90a4ec70",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mval: \u001b[0mdata=data/kitti.yaml, weights=['runs/train/exp61/weights/last.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.6, max_det=300, task=test, device=, workers=8, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=False, project=runs/val, name=exp, exist_ok=False, half=False, dnn=False\n",
      "YOLOv5 🚀 68de71e8 Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA vGPU-32GB, 32260MiB)\n",
      "\n",
      "Fusing layers... \n",
      "YOLOv5s_kitti summary: 157 layers, 7031701 parameters, 0 gradients, 15.8 GFLOPs\n",
      "\u001b[34m\u001b[1mtest: \u001b[0mScanning /root/autodl-tmp/datasets/kitti/labels/test.cache... 2244 images,\u001b[0m\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       2244      12198      0.843       0.64      0.738      0.464\n",
      "                   Car       2244       8711       0.91      0.786      0.891      0.642\n",
      "                   Van       2244        861      0.827      0.663      0.771      0.527\n",
      "                 Truck       2244        333      0.913      0.785      0.864      0.638\n",
      "                  Tram       2244        138      0.952      0.652      0.815      0.473\n",
      "            Pedestrian       2244       1286      0.773      0.596      0.679      0.354\n",
      "        Person_sitting       2244         89      0.662      0.616      0.641       0.34\n",
      "               Cyclist       2244        496      0.894      0.438      0.577      0.329\n",
      "                  Misc       2244        284      0.812      0.581      0.669       0.41\n",
      "Speed: 0.0ms pre-process, 0.9ms inference, 1.0ms NMS per image at shape (32, 3, 640, 640)\n",
      "Results saved to \u001b[1mruns/val/exp65\u001b[0m\n",
      "Test set val successfully!\n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "开始测baseline\n",
    "首先是基于无雾数据集的测试集\n",
    "'''\n",
    "# 这是无雾训练集\n",
    "model = f'runs/train/exp61/weights/last.pt'\n",
    "\n",
    "val_command = f\" \\\n",
    "python val.py \\\n",
    "--data data/kitti.yaml \\\n",
    "--weights {model} \\\n",
    "--task test &&\\\n",
    "echo 'Test set val successfully!' \\\n",
    "\" \n",
    "!{val_command}\n",
    "\n",
    "# ewc强度0.5\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3cec44e2-91d1-4c85-8ee3-aa064cd2f2ca",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4b1be15a-9039-44d5-bea0-32778653ee6b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "765ce2f8-9f0c-41c1-8b88-aa2fa30acdc3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "61da9560-355a-4ea5-a86c-5ca698e00d81",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "49861956-abba-4161-a939-802679243d51",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 然后是0.6雾测试集\n",
    "update_testsets = f\" \\\n",
    "rm ../datasets/kitti/images/test/* &&\\\n",
    "cp /root/autodl-tmp/datasets/fogged/fogged_strength1.0/* ../datasets/kitti/images/test/ && \\\n",
    "echo 'Test set updated successfully!' \\\n",
    "\" \n",
    "!{update_testsets}\n",
    "\n"
   ]
  }
 ],
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